GLM-4.5V vs Nemotron Research Reasoning
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GLM-4.5V by Zhipu AI and Nemotron Research Reasoning by NVIDIA represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, Nemotron Research Reasoning provides an expanded memory horizon with 128K tokens. In latency-sensitive production environments, GLM-4.5V leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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GLM-4.5V
Modello a Pesi ApertiGLM-4.5V di Zhipu AI è un modello di intelligenza artificiale con finestra di contesto da 65K token e tariffazione API di $0/1M token in input e $0/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.00 / 1M
$0.00 / 1M
65.5K tokens
N/A
Nemotron Research Reasoning
Modello a Pesi ApertiNemotron Research Reasoning di NVIDIA è un modello di intelligenza artificiale con finestra di contesto da 128K token e tariffazione API di $0/1M token in input e $0/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.00 / 1M
$0.00 / 1M
128K tokens
48.5%
Differenze Chiave a Colpo d’Occhio
- Both models cost the same under this workload — choose on capability, not price.
- Nemotron Research Reasoning has a larger context window (128K vs 66K tokens).
- GLM-4.5V is faster at 85 tok/s vs 45 tok/s.
| Feature | GLM-4.5V | Nemotron Research Reasoning |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 73 | 68 |
Provider | Zhipu AI | NVIDIA |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 65.5K tokens~98 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 4K | 16K |
Throughput Speed Tokens generated per second | 85 tok/s | 45 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 48.5% |
GPQA Diamond Graduate-level reasoning | N/A | 62% |
Chi vince in ogni categoria?
At-a-glance decision frameworkEvaluations ongoing
Punteggi Benchmark Verificati
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GLM-4.5V · Strengths & Key Advantages
- Architettura Open Multimodal Vision-Language Transformer avanzata di Zhipu AI con supporto fino a 65K token di contesto
- Ottimo rapporto qualità-prezzo ($0/1M input, $0/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Nemotron Research Reasoning · Strengths & Key Advantages
- Architettura Research-Grade Dense Transformer avanzata di NVIDIA con supporto fino a 128K token di contesto
- Ottimo rapporto qualità-prezzo ($0/1M input, $0/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GLM-4.5V | Nemotron Research Reasoning |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 8K | 18K |
Reasoning Tokens / Task Thinking tokens burned per task | 2K | 12K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-4.5V | Nemotron Research Reasoning |
|---|---|---|
| Official API Model ID (SDK) | glm-4-5v | nemotron-research-reasoning |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-08 | 2025-08 |
| Knowledge Cutoff | June 2025 | July 2025 |
| Architecture | Open Multimodal Vision-Language Transformer | Research-Grade Dense Transformer |
| Parameters | 12B VLM | 70B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 7 GB VRAM (4-bit Quantized) · 26 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
GLM-4.5V vs Nemotron Research Reasoning: Risposte Rapide (FAQ)
Which is more intelligent, GLM-4.5V or Nemotron Research Reasoning?
GLM-4.5V is more intelligent. It scores 73 compared with 68 on the composite Intelligence Index.
Which model is faster?
GLM-4.5V is faster, generating 85 tokens per second compared with 45 tokens per second.
Which model has lower latency?
GLM-4.5V has lower latency, with a time to first token of 0.48s compared with 0.52s.
Which model has a larger context window?
Nemotron Research Reasoning has a larger context window, supporting 128K tokens compared with 65.5K tokens.
Verdetto
Verdict & Model Selection Recommendation: Choose GLM-4.5V if your priority is Local edge document scanning, invoice extraction, and visual UI inspection. Its performance in demanding tasks justifies its operational token cost. Choose Nemotron Research Reasoning if your workflow prioritizes Scientific research, complex data analysis, and academic reasoning workflows. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Domande Frequenti (FAQ)
How does GLM-4.5V compare to Nemotron Research Reasoning in API token pricing and cost per token?+
Both GLM-4.5V and Nemotron Research Reasoning are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: GLM-4.5V or Nemotron Research Reasoning?+
GLM-4.5V supports a context window of 66K tokens (~49k words), while Nemotron Research Reasoning supports 128K tokens (~96k words). Therefore, Nemotron Research Reasoning can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GLM-4.5V or Nemotron Research Reasoning?+
GLM-4.5V scores at the frontier of coding benchmarks, while Nemotron Research Reasoning scores 48.5%. For complex autonomous debugging and agentic engineering, Nemotron Research Reasoning provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between GLM-4.5V and Nemotron Research Reasoning?+
GLM-4.5V features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Nemotron Research Reasoning provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GLM-4.5V and Nemotron Research Reasoning?+
GLM-4.5V generates approximately 85 tokens per second, while Nemotron Research Reasoning delivers 45 tokens per second. In latency-sensitive production environments, GLM-4.5V provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GLM-4.5V and Nemotron Research Reasoning to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to GLM-4.5V, and route deep architectural or complex reasoning queries to GLM-4.5V. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Fonti ufficiali: GetInfoUs Independent AI Benchmarks & Official API Specs